SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 651–675 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Active Human Pose EstimationCode1
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply ChainsCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Active Reinforcement Learning for Robust Building ControlCode1
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest OverfittingCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
AWAC: Accelerating Online Reinforcement Learning with Offline DatasetsCode1
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
Deep Reinforcement Learning for Active High Frequency TradingCode1
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
Deep Reinforcement Learning based Group Recommender SystemCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Asynchronous Methods for Deep Reinforcement LearningCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as ObservationCode1
Faster Deep Reinforcement Learning with Slower Online NetworkCode1
DeepMind Lab2DCode1
DeepMind Control SuiteCode1
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character SkillsCode1
Deep Policies for Online Bipartite Matching: A Reinforcement Learning ApproachCode1
Deep Reinforcement Learning at the Edge of the Statistical PrecipiceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified